Citation: | LYU Yanxia, LI Wenjie, WANG Yue, et al., “RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1093-1101, 2020, doi: 10.1049/cje.2020.09.010 |
Y.L. Zhang, J. Zhou, W. Zheng, et al., "Distributed deep forest and its application to automatic detection of cash-out fraud", CoRR, arXiv:/1805.04234, 2018.
|
Y.G. Qian, "Network traffic anomaly detection based on maximum entropy model", Chinese Journal of Electronics, Vol.16, No.3, pp.579-582, 2012.
|
D.B. Yuan, H. Li, F. Wang, et al., "A GNSS acquisition method with the capability of spoofing detection and mitigation", Chinese Journal of Electronics, Vol.27, No.1, pp.213-222, 2018.
|
J.L.P. Lima, D. Macêdo and C. Zanchettin, "Heartbeat anomaly Detection using adversarial oversampling", arXiv e-prints, 2019.
|
K.M. Ting, G.T. Zhou and F.T. Liu, "Mass estimation", Machine Learning, Vol.90, No.1, pp.127-160, 2013.
|
F.T. Liu, K.M. Ting and Z.H. Zhou, "Isolation forest", Proc. of IEEE International Conference on Data Mining, Washington, DC, USA, pp.413-422, 2008.
|
S. Aryal, K.M. Ting, J.R. Wells, et al., "Improving iForest with relative mass", Lecture Notes in Computer Science, Springer, Cham, pp.510-521, 2014.
|
Tongfeng Sun, Shifei Ding, Pin Li, et al., "Acomparative study of neural-network feature weighting", Artificial Intelligence Review, Vol.52, No.1, pp.469-493, 2019.
|
N. Zhang, et al., "Multi-view RBM with posterior consistency and domain adaptation", Information Sciences, Vol.516, pp.142-157, 2020.
|
J. Wen, H.J. Wang, J. Deng, et al., "Abnormal event detection based on deep learning", ACTA Electronica Sinica, Vol.48, No.02, pp.308-313, 2020.
|
N. Chawla and W. Wang, "Outlier detection with autoencoder ensembles", SIAM International Conference on Data Mining, pp.90-98, 2017.
|
Y. Zhao and M.K. Hryniewicki, "XGBOD:Improving supervised outlier detection with unsupervised representation learning", 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, 2018.
|
K.M. Ting, Y. Zhu and Z.H. Zhou, "Isolation kernel and its effect on SVM", Proc. of the 24th ACM SIGKDD International Conference, 2018.
|
T.B. Wang, F.B. Zhang and C.H. Xia, "Research on loophole with second distribution of real value detectors", Chinese Journal of Electronics, Vol.25, No.06, pp.155-164, 2016.
|
C.L. Wen, F.N. Zhou, C.B. Wen, et al., "An extended multi-scale principal component analysis method and application in anomaly detection", Chinese Journal of Electronics, Vol.21, No.3, pp.471-476, 2012.
|
S. Li, X.F. Zhou, H.B. Shi, et al., "Monitoring of multimode processes based on subspace decomposition", Industrial and Engineering Chemistry Research, Vol.54, No.15, pp.3855-3864, 2015.
|
J. Zhang, H. Wang, "Detecting outlying subspaces for high-dimensional data:The new task, algorithms, and performance", Knowledge and Information Systems, Vol.10, No.3, pp.333-355, 2006.
|
P. Kaur, M. Kumar and A. Bhandari, "A review of detection approaches for distributed denial of service attacks", Systems Science and Control Engineering, Vol.5, No.1, pp.301-320, 2017.
|
M.M. Breunig, H.P. Kriegel and R.T. Ng, "LOF:identifying density-based local outliers", Proc. of ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA, pp.93-104, 2000.
|
H. Zhao, H. Liu, Z. Ding, et al., "Consensus regularized multi-view outlier detection", IEEE Transactions on Image Processing, Vol.27, No.1, Page 236, 2018.
|
F.P. Guo and H.X. Hui, "Anomaly detection algorithm based on the local distance of density-based sampling data", Journal of Software, 2017.
|
F.T. Liu, K.M. Ting and G.T. Zhou, "On detecting clustered anomalies using SCiForest", Proc. of European Conference on Machine Learning and Knowledge Discovery in Databases, Barcelona, Spain, pp.274-290, 2010.
|
R.N. Calheiros, K. Ramamohanarao, R. Buyya, et al., "On the effectiveness of isolation-based anomaly detection in cloud data", Concurrency and Computation:Practice and Experience, DOI:10.1002/cpe.4169, 2017
|
S. Hariri, M.C. Kind and R.J. Brunner, "Extended isolation forest", IEEE Transactions on Knowledge and Data Engineering, pp.1-12, 2019.
|
R. Williamson, A. Smola, J. Shawe-Taylor, et al., "Support vector method for novelty detection", Proc. of International Conference on Neural Information Processing Systems, Denver, Colorado, USA, pp.582-588, 1999.
|
D. Droghini, D. Ferretti, E. Principi, et al., "A combined oneclass SVM and template-matching approach for user-aided human fall detection by means of floor acoustic features", Computational Intelligence and Neuroscience, pp.1-13, 2017
|
F.T. Liu, K.M. Ting and Z.H. Zhou, "Isolation-based anomaly detection", ACM Transactions on Knowledge Discovery from Data, Vol.6, No.1, pp.1-39, 2012.
|
W. Budgaga, M. Malensek, S.L. Sangmi, P. Shrideep, "A framework for scalable real-time anomaly detection over voluminous, geospatial data streams", Concurrency and Computation:Practice and Experience, Vol.29, No.12, DOI:10.1002/cpe.4106, 2017.
|
K. Yamanishi, J.I. Takeuchi, G. Williams, et al., "On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms", Data Mining and Knowledge Discovery, Vol.8, No.3, pp.275-300, 2000.
|
H.A. Futuhul, H. Moh and S. Halimatus, "Handling outlier in two-ways table data:the robustness of row-column interaction model", Journal of Physics:Conference Series, Vol.1028, DOI:10.1088/1742-6596/1028/1/012222, 2018.
|